Mobile monitoring reveals congestion penalty for vehicle emissions in London

Mobile air pollution measurements have the potential to provide a wide range of insights into emission sources and air pollution exposure. The analysis of mobile data is, however, highly challenging. In this work we develop a new regression-based framework for the analysis of mobile data with the aim of improving the potential to draw inferences from such measurements. A quantile regression approach is adopted to provide new insight into the distribution of NOx and CO emissions in Central and Outer London. We quantify the emissions intensity of NOx and CO (Delta NOx/Delta CO2 and Delta CO/Delta CO2) at different quantile levels (tau) to demonstrate how transient highemission events can be examined in parallel to the average emission characteristics. We observed a clear difference in the emissions behaviour between both locations. On average, the median (tau = 0.5) Delta NOx/Delta CO2 in Central London was 2x higher than Outer London, despite the stringent emission standards imposed throughout the Ultra Low Emissions Zone. A comprehensive vehicle emission remote sensing data set (n approximate to 700,000) is used to put the results into context, providing evidence of vehicle behaviour which is indicative of poorly controlled emissions, equivalent to high-emitting classes of older vehicles. Our analysis suggests the coupling of a dieseldominated fleet with persistently congested conditions, under which the operation of emissions aftertreatment technology is non-optimal, leads to increased NOx emissions.